### OpenClaw与Python深度集成指南 OpenClaw作为本地化AI代理框架，与Python的结合使用主要通过API调用、模型集成和自定义技能开发三个核心维度实现。以下将详细解析具体的技术实现路径和实践方案。 #### 一、Python调用OpenClaw API的完整流程 OpenClaw Gateway提供RESTful API接口，Python可通过HTTP请求直接调用本地AI服务[ref_2]。以下是完整的代码示例： ```python import requests import json class OpenClawClient: def __init__(self, base_url="http://127.0.0.1:18789"): self.base_url = base_url self.headers = {"Content-Type": "application/json"} def send_message(self, message, model_alias="default"): """发送消息到OpenClaw并获取响应""" payload = { "message": message, "model_alias": model_alias, "session_id": "python_client_001" # 保持会话连续性 } try: response = requests.post( f"{self.base_url}/api/v1/chat", headers=self.headers, data=json.dumps(payload), timeout=30 ) response.raise_for_status() return response.json()["response"] except requests.exceptions.RequestException as e: return f"API调用失败: {str(e)}" # 使用示例 client = OpenClawClient() response = client.send_message("用Python帮我写一个快速排序算法") print(f"AI回复: {response}") ``` **关键技术点说明**： - **端口配置**：默认使用18789端口，可通过Gateway配置文件修改[ref_3] - **会话管理**：通过session_id维持对话上下文，支持多轮对话 - **模型选择**：model_alias参数支持切换不同的大模型后端[ref_6] #### 二、Python集成Ollama本地模型的方案 OpenClaw支持通过Ollama部署本地大模型，Python可以直接调用这些模型[ref_1]。以下是集成配置示例： ```python import subprocess import requests class OllamaPythonBridge: def __init__(self, model_name="qwen3:32b"): self.model_name = model_name self.ollama_url = "http://localhost:11434" def ensure_model_loaded(self): """确保Ollama模型已加载""" try: check_cmd = ["ollama", "list"] result = subprocess.run(check_cmd, capture_output=True, text=True) if self.model_name not in result.stdout: # 自动下载模型 pull_cmd = ["ollama", "pull", self.model_name] subprocess.run(pull_cmd, check=True) except subprocess.CalledProcessError as e: print(f"模型加载失败: {e}") def generate_response(self, prompt): """通过Ollama生成回复""" self.ensure_model_loaded() payload = { "model": self.model_name, "prompt": prompt, "stream": False } response = requests.post( f"{self.ollama_url}/api/generate", json=payload ) return response.json()["response"] # 集成到OpenClaw配置 ollama_bridge = OllamaPythonBridge() openclaw_config = { "models": { "local_ollama": { "type": "ollama", "endpoint": "http://localhost:11434", "model": "qwen3:32b" } } } ``` #### 三、Python开发OpenClaw自定义技能 OpenClaw支持Python编写自定义技能(Skill)，扩展AI代理的功能范围[ref_4]。以下是一个数据处理技能的完整示例： ```python # skills/data_processor.py import pandas as pd import numpy as np from datetime import datetime class DataProcessorSkill: def __init__(self): self.skill_name = "data_processor" self.description = "数据处理和分析技能" def handle_csv_analysis(self, file_path): """处理CSV文件分析请求""" try: df = pd.read_csv(file_path) analysis_result = { "row_count": len(df), "column_count": len(df.columns), "column_names": df.columns.tolist(), "data_types": df.dtypes.to_dict(), "basic_stats": df.describe().to_dict(), "missing_values": df.isnull().sum().to_dict() } return { "status": "success", "analysis": analysis_result, "timestamp": datetime.now().isoformat() } except Exception as e: return {"status": "error", "message": str(e)} def generate_report(self, analysis_data): """生成数据分析报告""" report = f""" 数据分析报告 ============ 记录数量: {analysis_data['row_count']} 字段数量: {analysis_data['column_count']} 字段信息: {chr(10).join([f'- {col}' for col in analysis_data['column_names']])} 统计摘要: {analysis_data['basic_stats']} """ return report # 注册技能到OpenClaw def register_skills(): processor = DataProcessorSkill() skill_registry = { "analyze_csv": { "handler": processor.handle_csv_analysis, "description": "分析CSV文件内容" }, "generate_data_report": { "handler": processor.generate_report, "description": "生成数据报告" } } return skill_registry ``` #### 四、Python与OpenClaw Gateway的配置集成 通过Python管理OpenClaw Gateway配置，实现动态模型切换和安全策略配置[ref_2]： ```python import yaml import os class OpenClawConfigManager: def __init__(self, config_path="./openclaw_config.yaml"): self.config_path = config_path def load_config(self): """加载OpenClaw配置""" with open(self.config_path, 'r', encoding='utf-8') as f: return yaml.safe_load(f) def update_model_config(self, new_models): """更新模型配置""" config = self.load_config() config['models'].update(new_models) with open(self.config_path, 'w', encoding='utf-8') as f: yaml.dump(config, f, allow_unicode=True) # 触发配置热加载 self.reload_gateway() def reload_gateway(self): """重新加载Gateway配置""" try: # 通过API触发配置重载 requests.post("http://127.0.0.1:18789/api/v1/reload") except: print("配置热加载需要Gateway支持") def setup_python_integration(self): """设置Python集成环境""" config = { 'gateway': { 'host': '127.0.0.1', 'port': 18789, 'cors_origins': ['http://localhost:3000'] # Python Web应用 }, 'models': { 'python_ollama': { 'type': 'ollama', 'endpoint': 'http://localhost:11434', 'model': 'qwen3:32b' } }, 'security': { 'api_keys': ['python_integration_key'], 'allowed_origins': ['http://localhost:3000'] } } with open(self.config_path, 'w', encoding='utf-8') as f: yaml.dump(config, f, allow_unicode=True) # 使用配置管理器 config_manager = OpenClawConfigManager() config_manager.setup_python_integration() ``` #### 五、实际应用场景示例 **场景1：自动化数据分析流水线** ```python import schedule import time def daily_data_analysis(): """每日自动数据分析任务""" client = OpenClawClient() processor = DataProcessorSkill() # 分析最新数据文件 analysis_result = processor.handle_csv_analysis("./data/daily_sales.csv") # 生成报告摘要 report = processor.generate_report(analysis_result) # 通过OpenClaw发送分析洞察 insight_request = f""" 基于今日销售数据分析，请总结关键洞察： {report} """ ai_insight = client.send_message(insight_request) print(f"AI分析洞察: {ai_insight}") return ai_insight # 设置定时任务 schedule.every().day.at("09:00").do(daily_data_analysis) while True: schedule.run_pending() time.sleep(60) ``` **场景2：Python Web应用集成** ```python from flask import Flask, request, jsonify app = Flask(__name__) openclaw_client = OpenClawClient() @app.route('/ai-assistant', methods=['POST']) def ai_assistant(): """Web应用中的AI助手接口""" user_message = request.json.get('message', '') session_id = request.json.get('session_id', 'web_session') # 添加业务上下文 enhanced_prompt = f""" 作为业务助手，请处理以下用户请求： {user_message} 当前业务上下文： - 用户正在使用Web应用 - 需要提供专业、准确的回答 - 如涉及数据处理，请给出具体操作建议 """ response = openclaw_client.send_message(enhanced_prompt) return jsonify({"response": response, "session_id": session_id}) if __name__ == '__main__': app.run(port=3000, debug=True) ``` #### 六、最佳实践和注意事项 通过上述方案，Python与OpenClaw可以实现深度集成。关键成功因素包括：确保Ollama服务稳定运行[ref_3]、合理配置Gateway安全策略[ref_2]、实现错误处理和重试机制。这种集成方式特别适合需要本地化部署、数据隐私保护和自定义AI能力的应用场景[ref_4]。在实际部署时，建议使用Docker容器化部署以确保环境一致性[ref_5]，并通过完善的日志监控来保障系统稳定性。